The construction and analysis of m6A-related ceRNA network and patterns of immune infiltration in calcified aortic valve disease

doi:10.21203/rs.3.rs-1994500/v1

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The construction and analysis of m6A-related ceRNA network and patterns of immune infiltration in calcified aortic valve disease

https://doi.org/10.21203/rs.3.rs-1994500/v1

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Background

Despite being the most prevalent valve heart disease among the elderly, calcified aortic valve disease (CAVD) is not adequately addressed based on its current mechanisms. N6-methyladenosine (m6A) modification is increasingly being studied in cardiovascular disease. Nonetheless, the biological role of m6A in CAVD remains to be determined.

Methods

We obtained the differentially expressed m6A based on difference analysis, and identified the target genes regulated by key m6A through co-expression analysis and m6A2Target database. The enrichment analysis of targeted genes was performed via Metascape. Immunocyte infiltration analysis was performed by R-package. The tools such as miRDB, Targetscan, miRTarBase, and Cytoscape were applied for the construction of competitive endogenous RNAs (ceRNAs) network. Quantitative real-time PCR (qRT-PCR) was utilized to verify whether the expression of components in the ceRNA network is consistent with the public database.

Results

The ceRNA network consists of one m6a (KIAA1429), one mRNA (ZC3H12C), three miRNAs (miR-17-5p, miR-20b-5p and miR-137), and two lncRNAs (HCG11 and PRICKLE2-AS3). KIAA1429 is a down-regulated methyltransferase in CAVD. The genes modified by KIAA1429 were primarily enriched in the metabolic process of RNA, viral process, and immune system process et al. There was a rising infiltration of macrophages m0, and a decreasing infiltration of macrophages m2, dendritic cells resting in CAVD. KIAA1429 was negatively correlated with macrophages m0 and positively correlated with macrophages m2. KIAA1429(m6a), ZC3H12C (mRNA), and HCG11(lncRNA) showed lower expression levels in CAVD than those in normal tissue, whereas miR-17-5p, miR-20b-5p (miRNA) showed higher expression levels.

Conclusion

Potential pathways associated with KIAA1429 in CAVD were identified, in which ZC3H12C and miR-20b-5p might participate in CAVD progression via the nuclear factor kappa-B (NF-κB) pathway.

CAVD

m6A modification

immune infiltrations analysis

ceRNA

bioinformatics analysis

Calcified aortic valve disease (CAVD) is characterized by fibrocalcific remodeling and thickening of the aortic valve leaflets over time, leading to severe cardiac outflow restriction [1]. It is one of the major causes of reduced physical function, longevity, and quality of life today. Although rheumatic heart disease mortality has stayed stable since the twenty-first century, the mortality rate from CAVD has risen steadily over the past two decades [2]. Aortic valve sclerosis affects more than a quarter of persons over 65, with roughly 2% progressing from sclerosis to stenosis each year. After being diagnosed with CAVD, the majority of individuals have moderate to severe symptoms that necessitate therapy [3, 4]. There is no medicine that can treat or reduce disease development, so surgery was the only option available [5]. For patients who cannot tolerate thoracotomy, transcatheter aortic valve replacement and aortic valve implantation have become more practical and less hazardous choices [6]. It remains a heavy burden for patients to pay for surgery. Although Kraler S et al. [7] outlined key players in CAVD pathology, and described mechanisms involving impaired endothelial barrier function, promoted lipid homing, immune infiltration, and aberrant phospho-calcium metabolism, et al. Therapeutic strategies such as lipid-lowering therapy and aortic valve adverse remodeling therapeutic targets are proposed according to pathophysiological mechanisms. However, they are still proving effective only in animal experiments or have not yet been well tested in clinical trials [8]. The immune cells in the aortic valve, such as macrophages, T lymphocytes, and B lymphocytes, change with the progress of valve calcification. Adjusting adaptive immune cell signals may be a strategy for treating CAVD [9]. Researchers have tried to use immune modulation to treat other cardiovascular diseases in trials [10, 11]. It is necessarily better to understand the immune system’s role in valvular diseases and provide information on future treatment strategies to control the development of CAVD.

There are currently more than 170 chemical modifications of RNA found in different species [12]. N6-methyladenosine (m6A) modification has attracted extensive attention since its discovery in cancer cells in the 1970s [13]. Among mammals, m6A modification is the most common internal mRNA modification [14, 15]. The m6A modification is regulated by "writers" (methyltransferases), "readers" (reader proteins), and "erasers" (demethylases). The modifications to a given mRNA are determined by the m6A "writers" and "erasers". The "readers" identify the modifications and bind to them before passing the information to the downstream effectors. M6A regulators have been discovered as critical players in the RNA methylation process, influencing RNA activities, including stability, splicing, transport, translation, and degradation, et al [16]. Moreover, emerging evidence connected m6A regulator dysregulation to the onset and development of cardiometabolic illnesses such as hypertension, heart failure, ischemic heart disease, et al [17]. It also has been found that methyltransferase family member 3 (METTL3) promotes osteogenesis differentiation of aortic valve interstitial cells [18]. Although the findings suggest new molecular mechanisms for m6A modification in CAVD, the exact expression patterns and roles of other m6A regulators are unclear.

Even though it cannot encode proteins, non-coding RNA is crucial for tissue homeostasis and pathology [19]. Non-coding RNAs are mainly composed of long non-coding RNA (lncRNA) with more than 200 base pairs and microRNA (miRNA) with 21–24 base pairs, which play a vital role in cardiovascular diseases [20, 21]. Salmena et al. pioneered the competitive endogenous RNAs (ceRNAs) mechanism that lncRNA regulates downstream mRNA expression through sponge miRNA in 2011 [22]. A study has found that miR-137/TWIST11 axis inhibits osteoblast differentiation via the lncRNA OIP5-AS1 [23]. By upregulating Runx2, lncRNA TUG1-miR-204-5p promotes osteoblast differentiation [24]. Nonetheless, a comprehensive analysis of global m6A-related ceRNAs in CAVD is rare.

Bioinformatics has proven to be an effective and efficient method for identifying genetic and epigenetic changes associated with diseases [25]. We attempted to use the public database Gene Expression Omnibus (GEO) to investigate the significant pattern of m6A expression in CAVD tissue. In addition, we sought to build a comprehensive picture of m6A regulators in CAVD utilizing bioinformatics methods, including difference analysis, target gene prediction, enrichment analysis, Immune cell Infiltration Analysis, and ceRNA network construction. Finally, we confirmed the expression of components in m6A-related ceRNA in our clinical samples. This study may lead to more studies on the pathophysiology mechanism of CAVD, which is essential for finding effective targets for the pathophysiology and treatment of CAVD.

Acquisition and processing of data

Three matrix files of CAVD samples, which include GSE12644, GSE51472, and GSE83453, are all from the GEO (https://www.ncbi.nlm.nih.go-v/geo/). GSE12644, based on GPL570, is composed of 10 normal and 10 calcified aortic valves. GSE51472, based on GPL570, contains 5 normal, 5 sclerotic, and 5 calcified aortic valves. GSE83453, based on GPL10558, including 8 normal and 9 calcified aortic valves. To analyze more accurately, five sclerotic valve samples were removed from GSE51472. Currently, the most prevalent m6A methylation regulators consist of WTAP, KIAA1429, ZC3H13, RBM15, RBM15B, BLL1, METTL3, METTL14, METTL16, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, ELAVL1, IGF2BP1, FTO, ALKBH5, RBMX are regarded as common m6A regulators. To screen m6A regulators, the “limma” package was utilized to analyze samples from 3 datasets (|LogFC| >2 and P＜0.05). Heatmap diagrams were created to show the expression of m6A regulators in calcified and normal aortic valves. Following that, differentially expressed m6a in three datasets was defined as key CAVD-related m6A regulators.

Enrichment analysis of target genes of key CAVD-related m6A regulators

M6A2Target (http:// m6A2target.canceromics.org) was utilized to find the targets of key CAVD-related m6A. Based on GSE12644, Pearson correlation analysis was performed to identify co-expressed genes in CAVD (|R| >0.9 and P＜0.01). Target genes regulated by key m6A were selected when predicted targets and co-expressed genes were intersected. Then, Metascape was performed to functionally annotate genes regulated by key m6A. (http://Metascape.org/gp/index.html#/main/step1)

Immune Infiltration Analysis

CIBERSORT was utilized to analyze immune cell infiltration in CAVD, evaluated immune cell infiltration ratios, and constructed a bar graph for visual display. A comparison of infiltration of 22 kinds of immune cells into CAVD patients and healthy people was conducted using “vioplot” package. The correlation heatmap was then constructed using the "corrplot" tool to highlight the correlation of the 22 various types of invading immune cells. Spearman's rank examined the relationship between immune cells and a key m6A regulator. Finally, the "ggplot2" package was used to visualize the plot. P < 0.05 indicated statistically significant.

Competitive endogenous RNA (ceRNA) network

LncRNA modulates mRNA expression of encoded proteins by competing with mRNA for combining with miRNA. Based on this mechanism, we constructed the ceRNA network. The selection of differential Expressed lncRNAs and mRNAs in GSE12644 was performed by the “limma” package(P＜0.05 and |log2FC|＞1) and the DElncRNA-miRNA interaction was predicted by miRcode (http://www.mircode.org). Furthermore, we projected the potential target gene mRNA of miRNA using two online data bases: Targetscan (http://www.targetscan.org), and miRDB (http://www.mirdb.org). The differentially expressed mRNA was interacted with the target mRNA of the miRNA to obtain the miRNA-mRNA gene pool. Finally, we used Cytoscape software v3.8.1 to establish the corresponding lncRNA-miRNA-mRNA interaction network and visualize it. We did further research by crossing mRNA in ceRNA with target genes regulated by key CAVD-related m6A regulators to find their shared genes. The shared gene-related pathway may be a possible mechanism of m6A in CAVD.

Experimental design and qRT-PCR

Our specimens were from the Affiliated Hospital of Xuzhou Medical University, including 3 calcified aortic valves and 3 normal aortic valves. Three cases of calcified aortic valve were from men who underwent aortic valve replacement. Three normal samples were from aortic valve insufficiency due to mechanical stress in ascending aortic aneurysm. Senior pathologists examined all valve samples. Clinical information of these patients was provided in Supplement2. First, we made the specimens into tissue homogenate and then extracted the total RNA from our specimens by Trizol reagent (Invirogen). Secondly, the iScriptTM cDNA Synthesis Kit (Bio-Rad Laboratories, Hercules, CA, USA) was used for reverse transcription of the extracted total RNA to synthesize complementary DNA. Primers were designed and synthesized by Shanghai Generay Biotech Co, Ltd, and primer sequences could be found in Supplement2. Finally, a PCR reaction system was established. Details on the PCR primer sequences and their results can be obtained from the supplementary documentation.

Statistical analysis

Normal distributions are reported as mean ± standard deviation, non-normal distributions as median, and classification variables as percentages. Comparison of two groups using the t-test, Fisher test. Statistical analysis is conducted by SPSS 24.0 (IBM, Amunk, New York, USA) and Prism 7.0 (GraphPad, San Diego, CA, USA). P < 0.05 was considered statistically significant.

Identification of key m6A Regulators in CAVD and Healthy Samples

The study flowchart is shown in Fig.1. Three gene expression profiles were utilized to detect the critical m6A regulators via high-throughput data analysis. The results were presented in Fig. 2A-C and showed that 21 m6A regulators in the GSE12644, 7 m6A regulators in the GSE83453, and 14 m6A regulators in the GSE51472 were significantly different. The expression of KIAA1429 in CAVD tissue was lower than that in aortic valve tissue in all three datasets (Fig. 2D).

Enrichment analysis of Genes modified by CAVD-Related m6A Regulators

In CAVD tissues, KIAA1429 expression showed a remarkably high correlation with 1674 genes analyzed by co-expression analysis. Through m6A2Target, 2774 genes may be modified by KIAA1429. Thus, a total of 209 genes were possibly regulated by KIAA1429 through m6A methylation modification in CAVD. Besides, Meanwhile, the enrichment analysis of Metascape results (Fig. 3A, B) revealed that target genes were mainly involved in metabolic process of RNA, viral process, and immune system process, et al.

Immune infiltration analysis

The percentage of 22 kinds of immunocytes in every sample is shown in a bar plot (Fig. 4A). Compared with healthy samples, the expression of macrophages m2, dendritic cells resting in CAVD samples was lower, and the expression of macrophages m0 was higher (Fig. 4B). Monocytes were positively correlated to dendritic cells activated (r = 0.86) and T cells CD4 naive (r = 0.86). NK cells activated were positively associated with T cells CD4 naive (r = 0.96) and dendritic cells activated (r = 0.96). Whereas macrophages m0 were negatively correlated to macrophages m2 (r = -0.85) (Fig. 5A). KIAA1429 is positively correlated to macrophages m2 while negatively correlated to macrophages m0 (Fig. 5B).

CeRNA Network Construction for CAVD-Related m6A Regulators

There were 2 lncRNAs, 23 miRNAs, and 72 mRNAs shown in the ceRNA network using Cytoscape (Fig. 6A). First, miRcode database was utilized to find lncRNA-miRNA relationships. 2 DElncRNAs and 106 miRNAs combined to generate 118 lncRNA-miRNA pairings. In this study, we only consider interactions between miRNAs and mRNAs for which at least two of three databases are available, namely MiRTarBase, Targetscan, and miRDB. As a result, 72 DEmRNAs and 23 miRNAs were obtained, resulting in 151 miRNA-mRNA pairings. For further analysis, we crossed DEmRNA in ceRNA with genes modified by key CAVD-related m6A regulators and found that Zinc Finger CCCH-Type containing 12C (ZC3H12C) was their common gene. We picked out lncRNAs and miRNAs linked to ZC3H12C in ceRNAs. Finally, two lncRNAs, two mRNAs, and three miRNAs were combined to generate lncRNA-miRNA-mRNA pairings (Fig. 6B).

QRT-PCR

Comparing of normal and CAVD samples, qRT-PCR results showed that KIAA1429 and its regulated mRNA-ZC3H12C were significantly down-regulated (Fig. 7A). As to lncRNA, HCG11 was significantly down-regulated. While PRICKLE2-AS3 indicated no significant differences (Fig. 7B). Compared with normal tissue, miR-17-5p, miR-20b-5p expression levels were higher in CAVD. MiR-137 exhibited no significant differences (Fig. 7C).

Methylation of the m6A is an essential epigenetic marker that has been investigated extensively in cardiovascular disease [17]. Since m6A modification has gained interest in cardiovascular diseases in recent years, exploring the m6A regulatory factor in CAVD is still in its initial stages [18]. According to our knowledge, it was the first comprehensive bioinformatics investigation of the m6A modification in CAVD, and it may offer trustworthy guidelines for targeted experimental research into CAVD.

In our study, the expression profiles of common m6A regulators in CAVD tissue and normal tissue were compared by analyzing three datasets. A low expression of one "writer" (KIAA1429) was statistically significant by analyzing three datasets, suggesting it may have functional importance in CAVD. Methyltransferase complexes containing m6A writers, such as METTL3/4/14, WTAP and KIAA1429, enhanced the synthesis of m6A methylation. The largest component of the m6A methyltransferase complex, KIAA1429, is essential in m6A methylation [26]. KIAA1429 has been studied in cardiovascular diseases. For example, KIAA1429 affects the progression of aortic dissection in an m6A-dependent manner by regulating the maturation of Pri-miR-143-3p [27] and upregulates its expression in heart failure with preserved ejection [28].

To illustrate the functions and molecular mechanisms associated with the key m6A regulator, we selected genes that KIAA1429 may regulate through m6A methylation in CAVD. Metascape showed the enrichment analysis results of m6A-related genes, which were mainly concentrated in the metabolic process of RNA, viral process, immune system process, etc. Several of these pathways and functions are linked to the pathology and physiology of CAVD. For instance, m6A methylation regulates the metabolic process of mRNA, including splicing, decay, translation, and expression of mRNAs [29–31]. Viral infection plays a possible role in aortic stenosis pathogenesis, and pathogen-induced phenotypes in aortic valve interstitial cells evoke pro-inflammatory and pro-osteogenic responses [32]. Currently, CAVD is no longer viewed as a purely passive degenerative disease. An adaptive immune system, as well as an innate immune system play a role in the process of disease [33].

CAVD microenvironment was searched for immune cell infiltration using CIBERSORT. There was a rising infiltration of macrophages m0, and a decreasing infiltration of macrophages m2, dendritic cells resting, which could be linked to the occurrence and progression of CAVD. The results mentioned above are consistent with previous studies [34, 35]. Moreover, macrophages m0 were negatively related to macrophages m2. Macrophage-orchestrated chronic inflammation contributes to cardiovascular disease, including acceleration of the progression of CAVD. An imbalance in macrophages m2 polarization can alter the intensity of inflammatory responses, and its decrease has adverse effects on chronic inflammation of the valve [36]. Additionally, individuals with CAVD had an accumulation of NK cells in their valves and blood, connected to the presence of a valve pressure gradient [37]. Notably, the correlation analysis showed that the key m6A regulator KIAA1429 was negatively correlated with macrophages m0 and positively correlated with macrophages m2. As reported, differential expression of KIAA1429 is associated with the immune microenvironment in various tumours [38–40]. Similarly, mice with METTL3 deficiency have increased tumour-associated macrophage infiltration compared to mice with wild-type METTL3 [41]. From the perspective of bioinformatics, KIAA1429 may be associated with immune response in CAVD through macrophages m0 and macrophages m2.

In this study, the ceRNA network had 2 mRNAs, including ZC3H12C and KIAA1429. Their expressions in both our samples and databases are decreased. ZC3H12C regulates macrophage activation through inhibiting nuclear factor kappa-B (NF-κB) pathway activation [42]. Interestingly, KIAA1429 is highly correlated with macrophages m0 and macrophages m2. As the upstream regulatory factor of ZC3H12C, KIAA1429 may be involved in ZC3H12C regulation of macrophage activation through some unknown mechanism. The ceRNA network had 2 lncRNAs, including HCG11 and PRICKLE2-AS3. HCG11 has been shown to regulate disease through m6A methylation. Through IGF2BP2/LATS1, HCG11 mediated by METTL14 inhibits lung adenocarcinoma growth [43]. The PRICKLE2-AS3’s PCR results were negative, and the function of PRICKLE2-AS3 has not been reported in cardiovascular diseases [44]. HCG11 and PRICKLE2-AS3 regulated three miRNAs in the ceRNA networks: miR-17-5p, miR-20b-5p and miR-137. However, we did not find CAVD-related miRNA data in public databases to verify. Based on ceRNA network theory, miRNA expression should be increased. In pulmonary arterial smooth muscle cells (PASMC), inhibition of miR-17-5p inhibits PASMC proliferation and migration, while stimulation of miR-17-5p promotes collagen synthesis in PASMCs [45]. Due to the small sample size, our samples did not show positive results, despite in vitro experiments showing miR137 was involved in CAVD progression [23]. Hypoxia-induced apoptosis in cardiomyocytes is inhibited by miR-20b-5p through the NF-κB pathway [46]. Whether miR-20b-5p and ZC3H12C work together through the NF-κB pathway is worth exploring in CAVD. The ceRNA networks may offer new insights into the role of m6A in regulating CAVD. Finally, we identify the HCG11–miR-20b-5p–ZC3H12C axis associated with KIAA1429 might participate in the development of CAVD.

It is critical to recognize several limitations. First, we have primarily relied on public databases for our findings. Secondly, the sample size of the clinical tissues we collected is small. Third, there is a lack of stimulation or blocking ceRNA network nodes to observe whether there will be corresponding effects downstream. It is imperative to do much more research on the functional deficiency to clarify the mechanism and function of the m6A regulator in CAVD.

Utilizing a variety of bioinformatics tools, we analyzed the gene expression properties and probable activities of m6A regulators in human CAVD, and constructed regulatory networks for the key m6A regulator. Further evidence from clinical tissue revealed that HCG11–miR-20b-5p–ZC3H12C axis associated with KIAA1429 might participate in the development of CAVD. ZC3H12C and miR-20b-5p might participate in CAVD progression via the NF-κB pathway.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the Affiliated Hospital Xuzhou Medical University (XYFY2018 - KL007 - 01), patients or family members signed informed consent. All samples were collected and processed according to the Helsinki Declaration.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author (Email: [email protected]) on reasonable request. Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/.

Competing interests

The authors have no competing interest to declare.

Funding: This research was funded by the Jiangsu Provincial Science and Technology Department Social Development Fund (Grant number: BE2019639), Jiangsu Traditional Chinese Medicine Science and Technology Development Plan Project (Grant number: YB201988), Jiangsu Provincial Health Commission Project Fund (Grant number: M2020015), and Research and Practice Innovation Plan for Postgraduates in General Colleges and Universities in Jiangsu Province (Grant number: SJCX22_1267).

Author contributions

The study was designed by FD, JHC, and TDX; FD produced the first manuscript of the paper; XZX carried out the statistical analysis; XXS, ZXT, and LLW reviewed the manuscript; CFW, HCX, DYL, and XQL contributed to various sections. The project's administration was handled by TDX. The final manuscript was read and approved by all authors.

Acknowledgments

Not applicable.

Lindman BR, Clavel MA, Mathieu P, Iung B, Lancellotti P, Otto CM, Pibarot P: Calcific aortic stenosis. Nature reviews Disease primers 2016, 2:16006.
Coffey S, Roberts-Thomson R, Brown A, Carapetis J, Chen M, Enriquez-Sarano M, Zühlke L, Prendergast BD: Global epidemiology of valvular heart disease. Nature reviews Cardiology 2021, 18(12):853–864.
Otto CM, Prendergast B: Aortic-valve stenosis–from patients at risk to severe valve obstruction. The New England journal of medicine 2014, 371(8):744–756.
Eveborn GW, Schirmer H, Heggelund G, Lunde P, Rasmussen K: The evolving epidemiology of valvular aortic stenosis. the Tromsø study. Heart (British Cardiac Society) 2013, 99(6):396–400.
Bonow RO, Leon MB, Doshi D, Moat N: Management strategies and future challenges for aortic valve disease. Lancet (London, England) 2016, 387(10025):1312–1323.
Kraler S, Blaser MC, Aikawa E, Camici GG, Lüscher TF: Calcific aortic valve disease: from molecular and cellular mechanisms to medical therapy. European heart journal 2022, 43(7):683–697.
Myasoedova VA, Ravani AL, Frigerio B, Valerio V, Moschetta D, Songia P, Poggio P: Novel pharmacological targets for calcific aortic valve disease: Prevention and treatments. Pharmacological research 2018, 136:74–82.
Raddatz MA, Madhur MS, Merryman WD: Adaptive immune cells in calcific aortic valve disease. American journal of physiology Heart and circulatory physiology 2019, 317(1):H141-h155.
Ridker PM, Libby P, MacFadyen JG, Thuren T, Ballantyne C, Fonseca F, Koenig W, Shimokawa H, Everett BM, Glynn RJ: Modulation of the interleukin-6 signalling pathway and incidence rates of atherosclerotic events and all-cause mortality: analyses from the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS). European heart journal 2018, 39(38):3499–3507.
Thompson PL, Nidorf SM: Anti-inflammatory therapy with canakinumab for atherosclerotic disease: lessons from the CANTOS trial. Journal of thoracic disease 2018, 10(2):695–698.
Huang H, Weng H, Chen J: The Biogenesis and Precise Control of RNA m(6)A Methylation. Trends in genetics: TIG 2020, 36(1):44–52.
Desrosiers R, Friderici K, Rottman F: Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proceedings of the National Academy of Sciences of the United States of America 1974, 71(10):3971–3975.
Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M et al: Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 2012, 485(7397):201–206.
Ke S, Alemu EA, Mertens C, Gantman EC, Fak JJ, Mele A, Haripal B, Zucker-Scharff I, Moore MJ, Park CY et al: A majority of m6A residues are in the last exons, allowing the potential for 3' UTR regulation. Genes & development 2015, 29(19):2037–2053.
Shi H, Wei J, He C: Where, When, and How: Context-Dependent Functions of RNA Methylation Writers, Readers, and Erasers. Molecular cell 2019, 74(4):640–650.
Xu Z, Lv B, Qin Y, Zhang B: Emerging Roles and Mechanism of m6A Methylation in Cardiometabolic Diseases. Cells 2022, 11(7).
Zhou T, Han D, Liu J, Shi J, Zhu P, Wang Y, Dong N: Factors influencing osteogenic differentiation of human aortic valve interstitial cells. The Journal of thoracic and cardiovascular surgery 2021, 161(2):e163-e185.
Matsui M, Corey DR: Non-coding RNAs as drug targets. Nature reviews Drug discovery 2017, 16(3):167–179.
Barwari T, Joshi A, Mayr M: MicroRNAs in Cardiovascular Disease. Journal of the American College of Cardiology 2016, 68(23):2577–2584.
Uchida S, Dimmeler S: Long noncoding RNAs in cardiovascular diseases. Circulation research 2015, 116(4):737–750.
Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP: A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 2011, 146(3):353–358.
Zheng D, Wang B, Zhu X, Hu J, Sun J, Xuan J, Ge Z: LncRNA OIP5-AS1 inhibits osteoblast differentiation of valve interstitial cells via miR-137/TWIST11 axis. Biochemical and biophysical research communications 2019, 511(4):826–832.
Yu C, Li L, Xie F, Guo S, Liu F, Dong N, Wang Y: LncRNA TUG1 sponges miR-204-5p to promote osteoblast differentiation through upregulating Runx2 in aortic valve calcification. Cardiovascular research 2018, 114(1):168–179.
Gauthier J, Vincent AT, Charette SJ, Derome N: A brief history of bioinformatics. Briefings in bioinformatics 2019, 20(6):1981–1996.
Li A, Chen YS, Ping XL, Yang X, Xiao W, Yang Y, Sun HY, Zhu Q, Baidya P, Wang X et al: Cytoplasmic m(6)A reader YTHDF3 promotes mRNA translation. Cell research 2017, 27(3):444–447.
Wang P, Wang Z, Zhang M, Wu Q, Shi F, Yuan S: KIAA1429 and ALKBH5 Oppositely Influence Aortic Dissection Progression via Regulating the Maturation of Pri-miR-143-3p in an m6A-Dependent Manner. Frontiers in cell and developmental biology 2021, 9:668377.
Zhang B, Xu Y, Cui X, Jiang H, Luo W, Weng X, Wang Y, Zhao Y, Sun A, Ge J: Alteration of m6A RNA Methylation in Heart Failure With Preserved Ejection Fraction. Frontiers in cardiovascular medicine 2021, 8:647806.
Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D, Fu Y, Parisien M, Dai Q, Jia G et al: N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 2014, 505(7481):117–120.
Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, Weng X, Chen K, Shi H, He C: N(6)-methyladenosine Modulates Messenger RNA Translation Efficiency. Cell 2015, 161(6):1388–1399.
Xiao W, Adhikari S, Dahal U, Chen YS, Hao YJ, Sun BF, Sun HY, Li A, Ping XL, Lai WY et al: Nuclear m(6)A Reader YTHDC1 Regulates mRNA Splicing. Molecular cell 2016, 61(4):507–519.
López J, Fernández-Pisonero I, Dueñas AI, Maeso P, San Román JA, Crespo MS, García-Rodríguez C: Viral and bacterial patterns induce TLR-mediated sustained inflammation and calcification in aortic valve interstitial cells. International journal of cardiology 2012, 158(1):18–25.
Bartoli-Leonard F, Zimmer J, Aikawa E: Innate and adaptive immunity: the understudied driving force of heart valve disease. Cardiovascular research 2021, 117(13):2506–2524.
Steiner I, Krbal L, Rozkoš T, Harrer J, Laco J: Calcific aortic valve stenosis: Immunohistochemical analysis of inflammatory infiltrate. Pathology, research and practice 2012, 208(4):231–234.
He W, Che H, Jin C, Li Y, Li F, Zhou R: LncRNA AFAP1-AS1 promotes M1 polarization of macrophages and osteogenic differentiation of valve interstitial cells. Journal of physiology and biochemistry 2021, 77(3):461–468.
Zhou P, Li Q, Su S, Dong W, Zong S, Ma Q, Yang X, Zuo D, Zheng S, Meng X et al: Interleukin 37 Suppresses M1 Macrophage Polarization Through Inhibition of the Notch1 and Nuclear Factor Kappa B Pathways. Frontiers in cell and developmental biology 2020, 8:56.
Mazur P, Mielimonka A, Natorska J, Wypasek E, Gawęda B, Sobczyk D, Kapusta P, Malinowski KP, Kapelak B: Lymphocyte and monocyte subpopulations in severe aortic stenosis at the time of surgical intervention. Cardiovascular pathology: the official journal of the Society for Cardiovascular Pathology 2018, 35:1–7.
Wang L, Zhang S, Li H, Xu Y, Wu Q, Shen J, Li T, Xu Y: Quantification of m6A RNA methylation modulators pattern was a potential biomarker for prognosis and associated with tumor immune microenvironment of pancreatic adenocarcinoma. BMC cancer 2021, 21(1):876.
Zhao H, Xu Y, Xie Y, Zhang L, Gao M, Li S, Wang F: m6A Regulators Is Differently Expressed and Correlated With Immune Response of Esophageal Cancer. Frontiers in cell and developmental biology 2021, 9:650023.
Guo W, Tan F, Huai Q, Wang Z, Shao F, Zhang G, Yang Z, Li R, Xue Q, Gao S et al: Comprehensive Analysis of PD-L1 Expression, Immune Infiltrates, and m6A RNA Methylation Regulators in Esophageal Squamous Cell Carcinoma. Frontiers in immunology 2021, 12:669750.
Yin H, Zhang X, Yang P, Zhang X, Peng Y, Li D, Yu Y, Wu Y, Wang Y, Zhang J et al: RNA m6A methylation orchestrates cancer growth and metastasis via macrophage reprogramming. Nature communications 2021, 12(1):1394.
Liang J, Wang J, Azfer A, Song W, Tromp G, Kolattukudy PE, Fu M: A novel CCCH-zinc finger protein family regulates proinflammatory activation of macrophages. The Journal of biological chemistry 2008, 283(10):6337–6346.
Mao J, Qiu H, Guo L: LncRNA HCG11 mediated by METTL14 inhibits the growth of lung adenocarcinoma via IGF2BP2/LATS1. Biochemical and biophysical research communications 2021, 580:74–80.
Chen Z, Huang J, Feng Y, Li Z, Jiang Y: Profiling of specific long non-coding RNA signatures identifies ST8SIA6-AS1 AS a novel target for breast cancer. The journal of gene medicine 2021, 23(2):e3286.
Yao X, Jing T, Wang T, Gu C, Chen X, Chen F, Feng H, Zhao H, Chen D, Ma W: Molecular Characterization and Elucidation of Pathways to Identify Novel Therapeutic Targets in Pulmonary Arterial Hypertension. Frontiers in physiology 2021, 12:694702.
Zhou Z, Chen S, Tian Z, Deng S, Yi X, Yang S, Yang X, Jin L, Shi W: miR-20b-5p attenuates hypoxia-induced apoptosis in cardiomyocytes via the HIF-1α/NF-κB pathway. Acta biochimica et biophysica Sinica 2020, 52(9):927–934.

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The construction and analysis of m6A-related ceRNA network and patterns of immune infiltration in calcified aortic valve disease

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